Many challenges exist in auction pricing, particularly in used vehicle auction pricing. For example, one challenge is to accurately estimate the price a vehicle will command at auction given the subject vehicle's trim, options, condition, mileage and other factors that could potentially affect valuation of a subject vehicle. Due at least to the variability of data scarcity, size, and quality among disparate data sources and the computational power, memory, and data storage required, processing raw vehicle data can be cumbersome, time consuming, and prohibitively expensive. Moreover, while conventional auction pricing systems may provide simplified valuation mechanisms, they do not optimize auction valuations taking spatial (e.g., geographic location) and temporal (e.g., time-of-day, time-of-week, time-of-month, season-of-year) factors into account.
A conventional method qualitatively estimates wholesale values of used cars at a “global” or fixed level of vehicle grouping, independent of, e.g., location with respect to where the vehicle will be auctioned; e.g., all 2012 Ford Mustang GTs would be considered to have similar values. This method has several drawbacks. For example, the identified value may be inaccurate as a result of sparse data, especially for older vehicles. Additionally, if the location of the auction is ignored, fixed value estimates can be significantly inaccurate owing to substantial differences in demand for a given geographic market. For instance, the value of a 2014 Ford Mustang GT convertible being auctioned in Key West, Fla. may be significantly higher than the value it would command at auction in Estcourt Station, Me.
Another conventional method qualitatively estimates wholesale values for used cars at a fixed level of vehicle grouping, independent of, e.g., time-of-year. For instance, the value of a 2014 Ford Mustang GT convertible being auctioned in the month of June may be substantially higher than the value it would command at auction in January.
The inability to accurately determine the value of an asset, such as a used vehicle, can make it difficult for an asset owner, manager, or dealer such as an automobile dealer to buy and sell in a way that they can, with a degree of certainty, get a return on the maximum value of the asset. Consider an automobile dealer who must sell a given portion of used vehicle inventory at auction on, e.g., a quarterly basis to meet, e.g., lot turnover and year, make, model, trim distribution objectives. On one hand, the amount of profit realized by the automobile dealer will affect the decision to auction a particular vehicle or to try selling the vehicle at retail. On the other hand, maintaining a particular vehicle in inventory will have attendant costs relating to, e.g., transportation, repair, residence time on the dealer's lot, opportunity costs associated with another vehicle that might afford the dealer a larger profit, and/or the like.
The aforementioned predicament faced by an automobile dealer is only one example of what an asset owner, manager, or dealer may face, for instance, when a portfolio of assets or a portion thereof is up for auction or wholesale. When the price that each asset is likely to command at auction or for wholesale cannot be accurately determined, the individual pricing inaccuracies can add up to a significantly loss in profit. Furthermore, when an asset owner, manager, or dealer cannot accurately determine where and when an asset should be commissioned for auction or offer for sale to obtain the best price, the missed opportunities can significantly affect their bottom line. Consequently, there is room for innovations and improvements.